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sdrt: Estimating the Sufficient Dimension Reduction Subspaces in Time Series

The sdrt() function is designed for estimating subspaces for Sufficient Dimension Reduction (SDR) in time series, with a specific focus on the Time Series Central Mean subspace (TS-CMS). The package employs the Fourier transformation method proposed by Samadi and De Alwis (2023) <doi:10.48550/arXiv.2312.02110> and the Nadaraya-Watson kernel smoother method proposed by Park et al. (2009) <doi:10.1198/jcgs.2009.08076> for estimating the TS-CMS. The package provides tools for estimating distances between subspaces and includes functions for selecting model parameters using the Fourier transformation method.

Version: 1.0.0
Depends: R (≥ 3.5.0), stats
Imports: psych, tseries, pracma
Suggests: rmarkdown, knitr
Published: 2024-03-28
DOI: 10.32614/CRAN.package.sdrt
Author: Tharindu P. De Alwis ORCID iD [aut, cre], S. Yaser Samadi ORCID iD [ctb, aut]
Maintainer: Tharindu P. De Alwis <talwis at wpi.edu>
License: GPL-2 | GPL-3
NeedsCompilation: yes
Citation: sdrt citation info
Materials: NEWS
In views: TimeSeries
CRAN checks: sdrt results

Documentation:

Reference manual: sdrt.pdf
Vignettes: sdrt-vignette

Downloads:

Package source: sdrt_1.0.0.tar.gz
Windows binaries: r-devel: sdrt_1.0.0.zip, r-release: sdrt_1.0.0.zip, r-oldrel: sdrt_1.0.0.zip
macOS binaries: r-release (arm64): sdrt_1.0.0.tgz, r-oldrel (arm64): sdrt_1.0.0.tgz, r-release (x86_64): sdrt_1.0.0.tgz, r-oldrel (x86_64): sdrt_1.0.0.tgz

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.